Deep-learning-based evaluation and path optimization of teaching outcomes in lacquerware craft education

In the evolving landscape of art and design education, traditional craft teaching methods often struggle to objectively assess learning outcomes or guide curriculum transformation. This research proposes a deep learning (DL)-based model designed to assess the value of teaching outcomes and optimize the instructional transformation path in Lacquerware Craft Design education. The model leverages artificial intelligence to bridge subjective instructional appraisal with objective data analysis, thereby enhancing both educational quality and learner engagement. Data collection involved tracking student progress through structured practical sessions, classroom assessment, and peer reviews across multiple teaching cycles. Preprocessing steps included data normalization to ensure consistency and reliability. The model utilizes a Chaotic Dragonfly-driven Attention-based Recurrent Neural Network (CD-Att-RNN) to identify and enhance student learning patterns, aiming to improve instructional quality and effectiveness in the learning environment. The model assesses key features such as technique accuracy, creativity, material handling, and time efficiency. To optimize the transformation path, a feedback-driven learning mechanism is integrated, enabling continuous monitoring of teaching outcome trends and generating adaptive recommendations. The experiment was implemented in Python, and the CD-Att-RNN model demonstrates strong predictive performance, achieving a high Session Engagement Rate (90%) and Learning Satisfaction (91%) in assessing outcomes. Through iterative refinement, the transformation path is dynamically adjusted, ensuring sustained improvement in teaching quality over time. The model also offers practical lessons in pedagogy, thus empowering teachers to transform their lessons and provide personalized assistance to learners based on empirical evidence when designing curricula.